A Research on Interior Location of Improved Monte Carlo Algorithm Based on RSSI
L. Liang, Z. Tan
For the problem that the traditional static positioning algorithms can not locate the mobile network node accurately, and the traditional Monte Carlo localization algorithm features low
positioning accuracy and poor positioning accuracy due to the low sampling efficiency of the nodes, an improved Monte Carlo (Higher Monte Carlo, HMCL) localization algorithm is thereby proposed. The RSSI distance measured model is used to optimize the sampling weight and the position of the node is predicted by the least squares fitting nodes. At the same time, the cross algorithm is introduced to improve the particle activity; finally, the sampling area is optimized and the sampling interval is determined. It is found that the modified Monte Carlo algorithm can greatly reduce the number of sampling times and the positioning error of the nodes; at meanwhile, the positioning error is stable.